Advances in smart phone technologies along with their ever-increasing popularity have led to development of mobile crowdsourcing markets where workers are paid to perform various tasks. Mobile crowdsourcing differs from traditional crowdsourcing in that workers often need to be at specific contexts (such as location, transportation mode, surroundings, etc.) to perform assigned tasks using their smart phones or other mobile computing devices. Typical tasks pay workers a few dollars for capturing photos of buildings or places, checking price and placement of a product in stores, checking traffic, taking location-aware surveys, etc. Other location-based task examples include, but are not limited to detecting potholes, mapping noise and pollution in urban areas, monitoring road traffic, collecting speech samples, measuring personal environmental impact, etc.
Conventional mobile crowdsourcing schemes often provide generic platforms to enable various crowdsourcing tasks. Some of these platforms use various algorithms in an effort to improve task completion rates. For example, a common approach is to use various incentive-based mechanisms in an attempt to motivate workers to complete their tasks. Other approaches use reverse auction-based dynamic pricing algorithms, where mobile workers bid lower amounts for task completion to reduce the incentive cost compared with fixed task prices when assigning tasks to workers. A related approach uses a greedy algorithm based on a recurrent reverse auction incentive mechanism in an attempt to maximize a covered area under a budget constraint.